论文标题
指纹表现攻击检测：传感器和材料不可知的方法
Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach
论文作者
论文摘要
自动指纹识别系统对呈现攻击（即欺骗或改变手指）的脆弱性一直是一个越来越多的关注点，保证了准确有效的表现攻击检测（PAD）方法的发展。但是，现有垫解决方案的一个主要局限性是它们对新的PA材料和指纹传感器的概括不佳，未用于培训。在这项研究中，我们提出了一种可靠的跨材料和交叉传感器概括的稳健垫溶液。具体来说，我们建立在任何基于CNN的建筑之上，该体系结构训练了指纹欺骗检测，并使用样式转移网络包装器结合了跨物质欺骗概括。我们还将对抗性表示学习（ARL）纳入深神经网络（DNN），以学习PAD的传感器和材料不变表示。 Livdet 2015和2017年公共领域数据集的实验结果表现出了拟议方法的有效性。
The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i.e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods. However, one major limitation of the existing PAD solutions is their poor generalization to new PA materials and fingerprint sensors, not used in training. In this study, we propose a robust PAD solution with improved cross-material and cross-sensor generalization. Specifically, we build on top of any CNN-based architecture trained for fingerprint spoof detection combined with cross-material spoof generalization using a style transfer network wrapper. We also incorporate adversarial representation learning (ARL) in deep neural networks (DNN) to learn sensor and material invariant representations for PAD. Experimental results on LivDet 2015 and 2017 public domain datasets exhibit the effectiveness of the proposed approach.
